CN109979244B - Prediction method and device for airspace congestion of heterogeneous aircraft - Google Patents
Prediction method and device for airspace congestion of heterogeneous aircraft Download PDFInfo
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Abstract
The invention provides a heterogeneous aircraft airspace congestion prediction method and a heterogeneous aircraft airspace congestion prediction device. The prediction method for the airspace congestion of the heterogeneous aircraft, provided by the invention, can be used for rapidly calculating the congestion density of the airspace, and the prediction result has higher reliability and robustness compared with the prior art.
Description
Technical Field
The invention relates to a low-altitude airspace safety management technology, in particular to a method and a device for predicting airspace congestion of a heterogeneous aircraft.
Background
Civil aviation flight height is generally more than 6000 meters, and low-altitude airspace refers to a flight area below 6000 meters. The low-altitude airspace has heterogeneous aircrafts with different maneuverability, such as a general aviation aircraft, an unmanned aircraft, a paraglider, a power parachute, a blimp and a hot air balloon, the accurate prediction of the congestion condition of the low-altitude airspace is a precondition for the safety management of the low-altitude airspace, and important basis can be provided for the effective allocation of low-altitude airspace resources and the planning of an aircraft path.
In the prior art, the low-altitude airspace congestion condition is usually predicted based on the planned flight path of the aircraft, but due to the fact that disturbance factors such as random wind fields exist in the low-altitude airspace, the deviation between the planned flight path and the actual flight path of the aircraft is large, the accuracy of the prediction result of the low-altitude airspace congestion condition depending on the planned flight path of the aircraft is low, and the reliability is poor.
Therefore, an accurate and reliable method for predicting the jam of the heterogeneous aircraft in the low-altitude airspace is an urgent need for the safety management of the current low-altitude airspace.
Disclosure of Invention
The invention provides a prediction method and a prediction device for airspace congestion of a heterogeneous aircraft, which can not only quickly calculate the congestion density of an airspace, but also have robustness of a prediction result by considering disturbance factors such as a random wind field and the like on the basis of a planned flight path.
The invention provides a prediction method of airspace congestion of a heterogeneous aircraft, which comprises the following steps: according to a planned track and a predicted track of a heterogeneous aircraft, establishing an offline calculation model of a probability reachable set of the heterogeneous aircraft, wherein the predicted track is a predicted track of the heterogeneous aircraft flying in an airspace;
judging whether the probability reachable set region of the heterogeneous aircraft is overlapped with the region to be predicted or not according to the offline calculation model of the probability reachable set of the heterogeneous aircraft;
if the probability reachable set area of the heterogeneous aircraft is overlapped with the area to be predicted, establishing an airspace congestion prediction model of the heterogeneous aircraft according to the probability reachable set area of the heterogeneous aircraft corresponding to the planned track;
and obtaining congestion information in the area to be predicted according to the airspace congestion prediction model.
The invention provides a prediction device for airspace congestion of a heterogeneous aircraft, which comprises the following components:
the off-line computation model building module of the probability reachable set is used for building the off-line computation model of the probability reachable set of the heterogeneous aircraft according to the planned flight path and the predicted flight path of the heterogeneous aircraft, wherein the predicted flight path is the predicted flight path of the heterogeneous aircraft flying in an airspace;
the judging module is used for judging whether the probability reachable set region of the heterogeneous aircraft is overlapped with the region to be predicted or not according to the offline calculation model of the probability reachable set of the heterogeneous aircraft;
the airspace congestion prediction model establishing module is used for establishing an airspace congestion prediction model of the heterogeneous aircraft according to the probability reachable set area of the heterogeneous aircraft corresponding to the planned flight path when the probability reachable set area of the heterogeneous aircraft is overlapped with the area to be predicted;
and the acquisition module is used for acquiring the congestion information in the area to be predicted according to the airspace congestion prediction model.
The third aspect of the invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, the method for predicting the airspace congestion of the heterogeneous aircraft is realized.
The invention provides a prediction method of heterogeneous aircraft airspace congestion, which comprises the steps of establishing an offline calculation model of a probability reachable set of a heterogeneous aircraft by considering disturbance factors such as a random wind field on the basis of a planned flight path according to the planned flight path and a predicted flight path of the heterogeneous aircraft, judging whether the probability reachable set area of the heterogeneous aircraft is overlapped with an area to be predicted according to the offline calculation model of the probability reachable set of the heterogeneous aircraft, if so, establishing an airspace congestion prediction model of the heterogeneous aircraft according to the probability reachable set area of the heterogeneous aircraft corresponding to the planned flight path, and acquiring congestion information in the area to be predicted according to the airspace congestion prediction model; the prediction method for the airspace congestion of the heterogeneous aircraft, provided by the invention, can be used for rapidly calculating the congestion density of the airspace, and the prediction result has robustness.
Drawings
FIG. 1 is a schematic flow chart of a prediction method for airspace congestion of a heterogeneous aircraft provided by the invention;
FIG. 2 is a schematic flow chart of the present invention for determining whether a probability reachable set region of a heterogeneous aircraft overlaps with a region to be predicted;
FIG. 3 is a schematic flow chart of an offline calculation model for establishing a probabilistic reachable set of heterogeneous aircraft according to the present invention;
FIG. 4 is a first schematic structural diagram of a prediction apparatus for airspace congestion of a heterogeneous aircraft according to the present invention;
FIG. 5 is a structural schematic diagram of a prediction apparatus for airspace congestion of a heterogeneous aircraft according to the present invention;
fig. 6 is a third structural schematic diagram of the prediction device for airspace congestion of a heterogeneous aircraft provided by the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a schematic flow chart of a prediction method for airspace congestion of a heterogeneous aircraft provided by the present invention, and as shown in fig. 1, the method of this embodiment may include:
s101, establishing an off-line calculation model of the probability reachable set of the heterogeneous aircraft according to the planned flight path and the predicted flight path of the heterogeneous aircraft.
The invention discloses a low-altitude airspace with a plurality of aircrafts with different maneuvering performances, such as a general aviation aircraft, an unmanned aircraft, a paraglider, a power parachute, a blimp, a hot air balloon and the like. If the aircraft flies strictly according to a preset flight path when flying without the action of disturbance factors such as an external random wind field and the like, the formed flight track is the planned flight path of the aircraft, but because the disturbance factors such as the random wind field exist in a low-altitude airspace, the deviation between the actual flight path and the planned flight path of the aircraft is large, the actual flight path of heterogeneous aircrafts with different maneuverability under any wind field can not be mastered usually, so that a plurality of heterogeneous aircrafts exist in certain airspaces in the low-altitude airspace, and airspace congestion is caused; and some airspaces do not have heterogeneous aircrafts to fly, so that the waste of airspace resources is caused, and therefore the congestion condition of the airspace needs to be judged, namely the number of the heterogeneous aircrafts in the airspace needs to be judged, and the airspace is reasonably distributed.
According to the method, an off-line calculation model of the probability reachable set of the heterogeneous aircrafts is established according to the planned flight path and the predicted flight path of the heterogeneous aircrafts, wherein the predicted flight path is the flight path of the predicted heterogeneous aircrafts flying in an airspace. According to the invention, the simulator is used for simulating the heterogeneous aircraft to fly under the disturbance of external factors such as a random wind field and the like, so that the predicted flight path of the heterogeneous aircraft is obtained. The person skilled in the art can think that the real heterogeneous aircraft can also be adopted to fly under the disturbance of external factors such as different actual random wind fields and the like, so as to obtain the predicted flight path of the heterogeneous aircraft. The invention is not limited to the specific way of obtaining the predicted flight path of the heterogeneous aircraft.
After the planned flight path and the predicted flight path of the heterogeneous aircraft are obtained, an offline calculation model of the probability reachable set of the heterogeneous aircraft is established.
In this embodiment, the simulator is used to simulate the heterogeneous aircraft to fly under the action of external factors such as random wind fields, the flight trajectories of the heterogeneous aircraft in each wind field are simulated, the predicted trajectories of the heterogeneous aircraft obtained through simulation can be counted, and the flight trajectory database of the heterogeneous aircraft is established corresponding to the planned trajectories of the heterogeneous aircraft. Once a calculation model of the probability reachable set of the heterogeneous aircraft needs to be established, the planned flight path of the heterogeneous aircraft and the predicted flight path corresponding to the planned flight path can be searched from the database, the calculation model of the probability reachable set of the heterogeneous aircraft is performed offline, and the load of online calculation is reduced.
S102, judging whether the probability reachable set area of the heterogeneous aircraft is overlapped with the area to be predicted or not according to the offline calculation model of the probability reachable set of the heterogeneous aircraft.
The heterogeneous aircraft can form a flight area when flying in an airspace, the established offline calculation model of the probability reachable set of the heterogeneous aircraft can obtain the probability reachable set area of the heterogeneous aircraft, the congestion condition in the area to be predicted needs to be judged, namely whether the probability reachable set area of the heterogeneous aircraft is overlapped with the area to be predicted or not needs to be judged, and if the probability reachable set area of the heterogeneous aircraft is overlapped with the area to be predicted, the heterogeneous aircraft is judged to be in the area to be predicted.
Judging whether the probability reachable set region of the heterogeneous aircraft is overlapped with the region to be predicted, acquiring a space coordinate in the probability reachable set region and a space coordinate of the region to be predicted, comparing the space coordinate in the probability reachable set region and the space coordinate of the region to be predicted, and determining that the probability reachable set region is overlapped with the region to be predicted when the space coordinate in the probability reachable set region and the space coordinate of the region to be predicted have the same numerical value; and determining that the probability reachable set region overlaps with the region to be predicted by judging that the boundary of the probability reachable set region of the heterogeneous aircraft overlaps with the region to be predicted. A person skilled in the art may also determine whether the probability reachable set region of the heterogeneous aircraft overlaps with the region to be predicted in other ways, which is not limited in this embodiment.
S103, if the probability reachable set area of the heterogeneous aircraft is overlapped with the area to be predicted, establishing an airspace congestion prediction model of the heterogeneous aircraft according to the probability reachable set area of the heterogeneous aircraft corresponding to the planned track.
If the probability reachable set area of the heterogeneous aircraft is overlapped with the area to be predicted, an airspace congestion prediction model of the heterogeneous aircraft can be established, and the prediction model can be a model for obtaining the number of the heterogeneous aircraft in the area to be predicted or a model for obtaining the density of the heterogeneous aircraft in the area to be predicted. The embodiment is not limited to this, as long as the congestion condition in the area to be predicted can be reflected.
And S104, obtaining congestion information in the area to be predicted according to the airspace congestion prediction model.
As described above, the congestion information in the area to be predicted may be the number of heterogeneous aircrafts in the area to be predicted, or may also be the density of heterogeneous aircrafts in the area to be predicted, which is not limited in this embodiment.
According to the prediction method for the heterogeneous aircraft airspace congestion, an off-line calculation model of a probability reachable set of the heterogeneous aircraft is established according to a planned track and a predicted track of the heterogeneous aircraft, whether the probability reachable set area of the heterogeneous aircraft is overlapped with an area to be predicted or not is further judged, if the probability reachable set area of the heterogeneous aircraft is overlapped with the area to be predicted, an airspace congestion prediction model of the heterogeneous aircraft is established according to the probability reachable set area of the heterogeneous aircraft corresponding to the planned track, and congestion information in the area to be predicted is obtained according to the airspace congestion prediction model. The off-line calculation model of the probability reachable set is established off-line, so that on one hand, the calculation load is reduced, the congestion information of an airspace can be quickly obtained, on the other hand, disturbance factors such as a random wind field are considered on the basis of planning a flight path, and the reliability and robustness are higher compared with the prior art.
With reference to fig. 2, the following describes in detail whether the probability reachable set region of the heterogeneous aircraft overlaps with the region to be predicted. Fig. 2 is a schematic flow chart of the method for determining whether the probability reachable set region of the heterogeneous aircraft overlaps with the region to be predicted, as shown in fig. 2, the method includes:
s201, decomposing the probability reachable set region of the heterogeneous aircraft into a first vector set under a two-dimensional coordinate system, and decomposing the region to be predicted into a second vector set under the two-dimensional coordinate system.
In the prior art, whether the probability reachable set region overlaps with the region to be predicted or not can be judged by comparing spatial coordinates in the probability reachable set region and the region to be predicted or not, but the method adopted in the prior art is large in calculation amount and low in speed, and in order to quickly judge whether the probability reachable set region overlaps with the region to be predicted or not, the embodiment provides an approximate analysis calculation method, the probability reachable set region of the heterogeneous aircraft is decomposed into a first vector set C related to an X, Y axis in a two-dimensional coordinate system, and the region to be predicted is decomposed into a second vector set C' related to a X, Y axis in the two-dimensional coordinate system.
S202, acquiring the probability reachable of the heterogeneous aircraft by adopting a Minkowski method according to the first vector set and the second vector setVector sum region of set region and region to be predicted
Wherein the minkowski method can be represented by the following formula seven:
where C represents any vector in the first vector set C, and C 'represents any vector in the first vector set C'.
S203, judging whether a plurality of sample points in the planned flight path of the heterogeneous aircraft belong to a vector sum region; if yes, determining that the probability reachable set area of the heterogeneous aircraft is overlapped with the area to be predicted.
In this embodiment, both the probability reachable set region and the region to be predicted of the heterogeneous aircraft are decomposed into a vector set in a two-dimensional coordinate system, a minkowski method is used to obtain a vector sum region, whether the judgment on whether the probability reachable set region of the heterogeneous aircraft and the region to be predicted overlap or not is converted into a judgment on whether a plurality of sample points in the planned track of the heterogeneous aircraft belong to the vector sum region or not, and if the plurality of sample points in the planned track belong to the vector sum region, it is determined that the probability reachable set region of the heterogeneous aircraft overlaps the region to be predicted. The method avoids a complex space coordinate comparison method in the prior art, and can more quickly obtain the result of whether the probability reachable set region and the region to be predicted are overlapped.
Further, due to vectors and regions
The method is a complex polygonal area, and needs to adopt a complex numerical calculation method to judge whether a plurality of sample points in a planned flight path belong to a vector sum area
A method for avoiding numerical calculation of circumscribed ellipses of a polygonal area. Detailed description of the inventionThe method is as follows:
and acquiring a circumscribed elliptical area of the vector sum area.
Judging whether a plurality of sample points in the planned flight path of the heterogeneous aircraft belong to a circumscribed elliptical area or not; if yes, determining that a plurality of sample points in the planned flight path of the heterogeneous aircraft belong to the vector sum region.
In the embodiment, the external ellipse of which the vector and the area are complex polygonal areas is introduced, the problem of judging whether a plurality of sample points in the planned track belong to the vector and the area is converted into the problem of judging whether a plurality of sample points in the planned track of the heterogeneous aircraft belong to the external ellipse areas, the conversion utilizes the external ellipse to be relative to the complex polygonal areas, the calculation load is reduced, a method of complex numerical value calculation is avoided, whether a plurality of sample points in the planned track belong to the external ellipse areas can be judged more quickly, then the probability reachable set area of the heterogeneous aircraft is judged quickly to be overlapped with the area to be predicted, and then the congestion condition of the area to be predicted can be judged quickly.
The off-line computational model for establishing the probabilistic reachable set of heterogeneous aircraft is described in detail below with reference to FIG. 3. FIG. 3 is a schematic flow chart of the offline calculation model for establishing a probabilistic reachable set of heterogeneous aircraft according to the present invention, as shown in FIG. 3, the method includes:
s301, establishing a first ellipse probability reachable set model by taking a plurality of sample points in the planned flight path of the heterogeneous aircraft as the centers of circles respectively
The method comprises the steps of obtaining a planned flight path of the heterogeneous aircraft, wherein the planned flight path can be formed by a set of a plurality of sample points, establishing a first elliptic probability reachable set model by respectively taking the plurality of sample points as circle centers, and the first elliptic probability reachable set model is formed by a set of space coordinate points in a flight area of the plurality of elliptic heterogeneous aircraft.
S302, optimizing a shape matrix parameter theta in the first ellipse probability reachable set model through the opportunity constraint optimization model, and establishing a second ellipse probability reachable set model meeting a preset probability
Wherein the first elliptical probability reachable set region in the first elliptical probability reachable set model is an elliptical flight region, and the shape matrix of the ellipse is represented as
The shape matrix comprises a shape matrix parameter theta, and in order to enable the first elliptical probability reachable set model to be closer to a flight area formed by actual tracks of the heterogeneous aircraft, through an opportunity constraint optimization model, a matrix formed in the first elliptical probability reachable set area is equal to a transposed matrix of the first elliptical probability reachable set model
And predicting the track
Included in the first elliptical probability reachable set region
When the preset probability P is met, the optimized shape matrix parameter theta' is obtained, and at the moment, a second elliptic probability reachable set model is established
The opportunity constraint optimization model can be represented by the following formula I:
wherein M represents the number of types of the heterogeneous aircrafts, Sθ,m(tk)TA transposed area representing the first elliptical probability reachable set area; subject to indicates that the condition … … is satisfied, P indicates a preset probability,
representing a probability factor;
the predicted path is represented, wherein the uncertainty of the predicted path is represented.
S303, according to a plurality of sampling points in the predicted flight path, and by adopting a scene method, the shape matrix parameter theta' in the second ellipse probability reachable set model is approximately solved to obtain an optimal solution theta*Off-line calculation model for establishing probability reachable set of heterogeneous aircraft
In order to enable the second ellipse probability reachable set model to be closer to an ellipse area formed by the heterogeneous aircraft in actual flight, a simulator or other testing means is used for collecting and predicting a plurality of sampling points in the flight path, therefore, N sampling points in the predicted flight path are adopted and are substituted into a formula I, the shape matrix parameter theta' in the second ellipse probability reachable set model is approximately solved, the formula is converted into a classical convex optimization problem, and the optimal solution theta is obtained*。
In order to make the optimal solution theta obtained in the present embodiment*The confidence coefficient of the method is as large as possible, the number N of sampling points to be collected is as large as possible, the simulated predicted flight path is closer to the actual flight path of the heterogeneous aircraft, and the shape matrix parameter theta' in the second elliptic probability reachable set model is approximately solved by using a scene method to obtain a solution theta*The greater the confidence of (c).
Further, the number N of the plurality of sampling points in the predicted flight path provided by this embodiment satisfies the following formula two:
wherein d represents a shape matrix parameter θ*D may be 4-dimensional, 5-dimensional, etc. in the actual calculation process, β representing the confidence factor.
The operator may preset the probability P, i.e. the probability factor
The confidence 1- β may also be preset, i.e., the confidence factor β, in order to improve the finally obtained shape matrix parameter θ*β may take on very small values (e.g., 10)-10) Parameters to be preset
And β and the shape matrix parameter θ*The dimension d of the predicted flight path is substituted into a formula II, and the number N of sampling points of the predicted flight path, which need to be acquired under the conditions of certain preset probability and preset setting degree, can be obtained. Substituting the N sampling points into a formula I to obtain the optimal solution theta of the shape matrix parameter*. According to which the optimal solution theta*The confidence coefficient is high, so that the finally acquired offline calculation model of the probability reachable set of the heterogeneous aircraft has high confidence coefficient.
In this embodiment, the predicted flight path is included in the first elliptical probability reachable region by presettingThe preset probability P which is satisfied in the domain enables the shape matrix parameter theta in the first elliptic probability reachable set model to have a certain confidence coefficient after being optimized, the number N of sampling points of the predicted flight path to be acquired is obtained by presetting the preset probability P to have a certain confidence coefficient of 1- β, the N sampling points are brought into the first elliptic probability reachable set model, and the optimal solution theta is obtained*And obtaining an off-line calculation model of the probability reachable set of the heterogeneous aircraft with high confidence.
After obtaining the off-line calculation model of the probability reachable set of the heterogeneous aircraft with high confidence and the result of whether the probability reachable set region of the heterogeneous aircraft is overlapped with the region to be predicted, establishing an airspace congestion prediction model of the heterogeneous aircraft, which can be specifically represented by the following formula three:
wherein the content of the first and second substances,
indicating an airspace congestion index function, Ec(x,Sc) Representing a model of a region to be predicted, wherein the region to be predicted is a circular region, x represents the center of the circle of the region to be predicted, ScRepresenting the area to be predicted, NcIndicating the number of heterogeneous aircraft present in the area to be predicted, D(x,tk) Indicating the congestion density of the area to be predicted.
The region to be predicted may be denoted S, assuming N in the region to be predictedcErecting a heterogeneous aircraft, and obtaining a circular area of the area to be predicted by taking any position x in the area to be predicted as a circle center, namely establishing an area model E to be predictedc(x,Sc) The congestion condition of the area to be predicted can be collected by the probability of the heterogeneous aircraft
And a model of the area to be predicted Ec(x,Sc) The number D of heterogeneous aircrafts that overlap in the circular area in (b)(x,tk) Is shown, e.g. asAnd the third formula is shown. When the probability reachable set area of the heterogeneous aircraft is overlapped with the circular area of the area to be predicted, the function value of the airspace congestion index function is 1, and otherwise, the function value is 0.
Furthermore, in the practical application process, the airspace congestion condition of the whole low-altitude airspace needs to be predicted, so that the low-altitude airspace can be subjected to grid division to obtain a plurality of altitude layers, and then whether the probability reachable set area of the heterogeneous aircraft is overlapped with the area to be predicted or not is judged for each altitude layer. The way to grid the low-altitude space domain can be as follows:
rasterizing the airspace in the height direction to obtain a plurality of height layers, wherein the height layers are for the airspace [ h ]s,hd]The method comprises the following steps of obtaining a prediction area to be predicted by uniformly sampling horizontal areas with the height of each center according to △ h, obtaining a plurality of prediction points, and obtaining an area to be predicted by taking each prediction point as a center of a circle, wherein the above embodiment describes the congestion condition of an airspace in an area to be predicted, and if the congestion condition of the whole low-altitude airspace is described, the following formula can be shown as follows:
j∈{1,...,J},k∈{1,...,ns},m∈{1,...,M}
wherein h issIndicates the height, h, corresponding to the lowest level of the space domaindRepresenting the height corresponding to the highest level of the airspace, △ h representing the height of each height level, hjJ1, J, wherein hjJ is the center height of each height layer, J is 1.
When the heterogeneous aircraft flies at a certain altitude layer in the actual airspace, the flying direction can face to multiple directions, that is, the heading angle is not constant, so in the above embodiment, when the offline calculation model of the probability reachable set of the heterogeneous aircraft is established, the influence of the heading angle on the offline model needs to be considered.
Wherein the heading angle psi of the heterogeneous aircraftiWhen the angle is equal to 0 °, the first scenario formula corresponding to the scenario method may be specifically represented by the following formula five:
and after the second elliptical probability reachable set model is obtained, applying a first scene formula to obtain an offline calculation model of the probability reachable set of the heterogeneous aircraft.
When heading angle psi of heterogeneous aircraftiWhen not equal to 0 °, the second scene formula corresponding to the scene method may be specifically shown as the following formula six:
and after the second elliptical probability reachable set model is obtained, applying a second scene formula to obtain an offline calculation model of the probability reachable set of the heterogeneous aircraft.
Wherein S isθ',m(tk)TTransposed area, R, representing the second elliptical probability reachable set areaψRepresenting the twiddle factor.
Specifically, the course angle is an included angle between a predicted flight path of the heterogeneous aircraft and the X-axis direction of the two-dimensional coordinate system of the altitude layer. The heading angle can also be defined as the included angle between the predicted flight path of the heterogeneous aircraft and the Y-axis direction of the two-dimensional coordinate system of the altitude layer or the included angle between the predicted flight path of the heterogeneous aircraft and the planned flight path, and only the corresponding rotation factor can be determined through the heading angle to obtain the correct offline calculation model of the probability reachable set of the heterogeneous aircraft. The present embodiment does not specifically limit the definition of the heading angle.
On the basis of a planned flight path, disturbance factors such as a random wind field are considered, a simulator and the like are used for obtaining a predicted flight path of the heterogeneous aircraft, an offline calculation model of a probability reachable set of the heterogeneous aircraft is established, and the reliability is higher compared with the prior art; whether the probability reachable set region of the heterogeneous aircraft is overlapped with the region to be predicted or not is judged and converted into whether the planned flight path belongs to the vector sum region corresponding to the probability reachable set region of the heterogeneous aircraft and the region to be predicted or not, so that the adoption of a complex numerical calculation method is avoided, and the overlapping result can be quickly obtained; and finally, establishing an airspace congestion prediction model of the heterogeneous aircraft. Due to the fact that the number of the sampling points in the predicted flight path enables the established offline calculation model of the probability reachable set of the heterogeneous aircraft to have high confidence, the obtained congestion information in the region to be predicted finally has high confidence and robustness.
Fig. 4 is a schematic structural diagram of a prediction apparatus for heterogeneous aircraft airspace congestion, where as shown in fig. 4, the prediction apparatus 400 for heterogeneous aircraft airspace congestion includes: the system comprises a probability reachable set offline calculation model establishing module 401, a judging module 402, an airspace congestion prediction model establishing module 403 and an obtaining module 404.
And the offline calculation model building module 401 for probability reachable sets is used for building an offline calculation model for probability reachable sets of the heterogeneous aircrafts according to the planned flight paths and the predicted flight paths of the heterogeneous aircrafts, wherein the predicted flight paths are predicted flight paths of the heterogeneous aircrafts flying in an airspace.
A judging module 402, configured to judge whether a probability reachable set region of the heterogeneous aircraft overlaps with a region to be predicted according to the offline calculation model of the probability reachable set of the heterogeneous aircraft.
And an airspace congestion prediction model establishing module 403, configured to establish an airspace congestion prediction model of the heterogeneous aircraft according to the probability reachable set area of the heterogeneous aircraft corresponding to the planned track when the probability reachable set area of the heterogeneous aircraft overlaps with the area to be predicted.
The obtaining module 404 is configured to obtain congestion information in the area to be predicted according to the airspace congestion prediction model.
The prediction apparatus in the embodiment shown in fig. 4 can be correspondingly used to perform the steps performed by the prediction method in the embodiment shown in fig. 1, and the implementation principle and the technical effect are similar, which are not described herein again.
Optionally, the determining module 402 is specifically configured to decompose the probability reachable set region of the heterogeneous aircraft into a first vector set in a two-dimensional coordinate system, and decompose the region to be predicted into a second vector set in the two-dimensional coordinate system; acquiring a vector sum region of a probability reachable region of the heterogeneous aircraft and a region to be predicted by adopting a Minkowski method according to the first vector set and the second vector set; judging whether a plurality of sample points in the planned flight path of the heterogeneous aircraft belong to a vector sum region or not; if yes, determining that the probability reachable set area of the heterogeneous aircraft is overlapped with the area to be predicted.
Optionally, the determining module 402 is specifically configured to obtain a vector and a circumscribed elliptical region of the region; judging whether a plurality of sample points in the planned flight path of the heterogeneous aircraft belong to a circumscribed elliptical area or not; if yes, determining that a plurality of sample points in the planned flight path of the heterogeneous aircraft belong to the vector sum region.
Optionally, the offline computation model building module 401 for probability reachable sets is specifically configured to build a first elliptical probability reachable set model with a plurality of sample points in the planned flight path of the heterogeneous aircraft as centers of circles respectively
Optimizing a shape matrix parameter theta in the first ellipse probability reachable set model through the opportunity constraint optimization model, and establishing a second ellipse probability reachable set model meeting the preset probability
According to a plurality of sampling points in the predicted flight path, and a scene method is adopted to approximately solve the shape matrix parameter theta' in the second ellipse probability reachable set model to obtain an optimal solution theta*Off-line calculation model for establishing probability reachable set of heterogeneous aircraft
The opportunity constraint optimization model can be represented by the following formula I:
wherein, tkRepresents the time of flight of the heterogeneous aircraft, k ∈ {1sDenotes the planned flight time range of the hetero-aircraft, nsRepresents the maximum value of the planned time-of-flight range;
Sθ,m(tk) Representing a first elliptical probability reachable set region, S, with a heterogeneous aircraftθ',m(tk) A second elliptical probabilistic reachable set region representing a heterogeneous aircraft,
an elliptical probability reachable set region in the offline calculation model representing the reachable set of probabilities of the heterogeneous aircraft, wherein N represents the number of sampling points in the predicted flight path; sθ,m(tk)TA transposed area representing the first elliptical probability reachable set area;
subject to indicates that the condition … … is satisfied, P indicates a preset probability,
representing a probability factor.
Optionally, the number N of sampling points in the predicted flight path satisfies the following formula two:
wherein d represents a shape matrix parameter θ*β denotes a confidence factor.
Optionally, the airspace congestion prediction model may be represented by the following formula three:
wherein the content of the first and second substances,
indicating an airspace congestion index function, Ec(x,Sc) Representing a model of a region to be predicted, wherein the region to be predicted is a circular region, x represents the center of the circle of the region to be predicted, ScRepresenting the area to be predicted, NcIndicating the number of heterogeneous aircraft present in the area to be predicted, D(x,tk) Indicating the congestion density of the area to be predicted.
Fig. 5 is a schematic structural diagram of a prediction apparatus for heterogeneous aircraft airspace congestion provided in the present invention, and as shown in fig. 5, the prediction apparatus 400 for heterogeneous aircraft airspace congestion may further include: a model building module 405 for the area to be predicted.
The to-be-predicted region model establishing module 405 is configured to perform rasterization processing on the airspace in the height direction to obtain a plurality of height layers, where the plurality of height layers are for the airspace [ h ]s,hd]The method comprises the steps of obtaining a plurality of prediction points by dividing the prediction points according to △ h, uniformly sampling horizontal areas with the height of each center to obtain a plurality of prediction points, and establishing an area to be predicted by taking each prediction point as a circle center.
Accordingly, the airspace congestion prediction model of the heterogeneous aircraft in the area to be predicted, which takes the circle center as the center, can be represented by the following formula four:
j∈{1,...,J},k∈{1,...,ns},m∈{1,...,M}
wherein h issIndicates the height, h, corresponding to the lowest level of the space domaindRepresenting the height corresponding to the highest level of the airspace, △ h representing the height of each height level, hjJ1, J, wherein hjJ is the center height of each height layer, J is 1The number of layers in which the space domain is located, J denotes the number of layers in the space domain.
Optionally, when the heading angle psi of the heterogeneous aircraftiWhen the angle is equal to 0 °, the first scenario formula corresponding to the scenario method may be specifically represented by the following formula five:
when heading angle psi of heterogeneous aircraftiWhen not equal to 0 °, the second scene formula corresponding to the scene method may be specifically shown as the following formula six:
wherein S isθ',m(tk)TTransposed area, R, representing the second elliptical probability reachable set areaψRepresenting the twiddle factor.
The principle and the technical effect of the prediction device for the airspace congestion of the heterogeneous aircraft provided by the embodiment are similar to those of the prediction method for the airspace congestion of the heterogeneous aircraft, and are not described herein again.
Fig. 6 is a schematic structural diagram of a third prediction apparatus for heterogeneous aircraft airspace congestion, provided by the present invention, where the prediction apparatus for heterogeneous aircraft airspace congestion may be a terminal device, such as a smart phone, a tablet computer, a computer, and the like. As shown in fig. 6, the prediction apparatus 500 for airspace congestion of heterogeneous aircraft includes: a memory 501 and at least one processor 502.
A memory 501 for storing program instructions.
The processor 502 is configured to implement the method for processing an unread message in this embodiment when the program instructions are executed, and the specific implementation principle may refer to the foregoing embodiment, which is not described herein again.
The prediction device for the airspace congestion of the heterogeneous aircraft can also comprise an input/output interface 503.
The input/output interface 503 may include a separate output interface and input interface, or may be an integrated interface that integrates input and output. The output interface is used for outputting data, the input interface is used for acquiring input data, the output data is a general name output in the method embodiment, and the input data is a general name input in the method embodiment.
The invention also provides a computer-readable storage medium, which stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, the method for predicting the airspace congestion of the heterogeneous aircraft provided by the embodiment is implemented.
The present invention also provides a program product comprising execution instructions stored in a readable storage medium. The at least one processor of the casing design apparatus may read the executable instructions from the readable storage medium, and the execution of the executable instructions by the at least one processor causes the casing design apparatus to implement the casing design methods provided by the various embodiments described above.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
The integrated unit implemented in the form of a software functional unit may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device) or a processor (processor) to execute some steps of the methods according to the embodiments of the present invention. And the aforementioned storage medium includes: a U disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In the foregoing embodiments of the network device or the terminal device, it should be understood that the Processor may be a Central Processing Unit (CPU), or may be another general-purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present application may be embodied directly in a hardware processor, or in a combination of the hardware and software modules in the processor.
Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions. The program may be stored in a computer-readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (9)
1. A prediction method for airspace congestion of a heterogeneous aircraft is characterized by comprising the following steps:
according to a planned track and a predicted track of a heterogeneous aircraft, establishing an offline calculation model of a probability reachable set of the heterogeneous aircraft, wherein the predicted track is a predicted track of the heterogeneous aircraft flying in an airspace;
judging whether the probability reachable set region of the heterogeneous aircraft is overlapped with the region to be predicted or not according to the offline calculation model of the probability reachable set of the heterogeneous aircraft;
if the probability reachable set area of the heterogeneous aircraft is overlapped with the area to be predicted, establishing an airspace congestion prediction model of the heterogeneous aircraft according to the probability reachable set area of the heterogeneous aircraft corresponding to the planned track;
acquiring congestion information in the area to be predicted according to the airspace congestion prediction model;
the method for establishing the off-line calculation model of the probability reachable set of the heterogeneous aircraft according to the planned flight path and the predicted flight path of the heterogeneous aircraft comprises the following steps:
establishing a first ellipse probability reachable set model by taking a plurality of sample points in the planned flight path of the heterogeneous aircraft as the circle centers respectively
Optimizing shape matrix parameters θ in the first elliptical probability reachable set model by an opportunistic constraint optimization modelEstablishing a second elliptic probability reachable set model satisfying the preset probability
According to a plurality of sampling points in the predicted flight path, and a scene method is adopted to approximately solve the shape matrix parameter theta' in the second ellipse probability reachable set model to obtain an optimal solution theta*Establishing an offline calculation model of the probabilistic reachable set of the heterogeneous aircraft
The opportunity constraint optimization model is shown as the following formula I:
wherein, tkRepresents the time of flight of the hetero-aircraft, k ∈ {1sDenotes the planned flight time range of the hetero-aircraft, nsRepresenting a maximum value of the planned time-of-flight range;
Sθ,m(tk) Representing an reachable set of regions with a first elliptical probability of said hetero-aircraft, Sθ',m(tk) A second elliptical probabilistic reachable set region representing the heterogeneous aircraft,
an elliptical probability reachable set region in an offline computational model representing the heterogeneous aircraft probability reachable set, wherein N represents the number of sampling points in the predicted flight path; sθ,m(tk)TA transpose region representing the first elliptical probability reachable set region;
subject to indicates that the condition … … is satisfied, P indicates the preset probability,
representing a probability factor.
2. The prediction method of claim 1, wherein the determining whether the probabilistic reachable set region of the heterogeneous aircraft overlaps with the region to be predicted comprises:
decomposing the probability reachable set region of the heterogeneous aircraft into a first vector set under a two-dimensional coordinate system, and decomposing the region to be predicted into a second vector set under the two-dimensional coordinate system;
acquiring a vector sum region of the probability reachable region of the heterogeneous aircraft and the region to be predicted according to the first vector set and the second vector set by adopting a Minkowski method;
judging whether a plurality of sample points in the planned flight path of the heterogeneous aircraft belong to the vector sum region or not;
if yes, determining that the probability reachable set area of the heterogeneous aircraft is overlapped with the area to be predicted.
3. The prediction method of claim 2, wherein the determining whether the plurality of sample points in the planned flight path of the heterogeneous aircraft belong to the vector sum region comprises:
acquiring a circumscribed elliptical area of the vector sum area;
judging whether a plurality of sample points in the planned flight path of the heterogeneous aircraft belong to the circumscribed elliptical area or not;
if yes, determining that a plurality of sample points in the planned flight path of the heterogeneous aircraft belong to the vector sum region.
4. The prediction method according to claim 1, wherein the number N of sampling points in the predicted flight path satisfies the following formula two:
wherein d represents the shape matrix parameter θ*β denotes a confidence factor.
5. The prediction method according to claim 4, wherein the airspace congestion prediction model is represented by the following formula three:
wherein the content of the first and second substances,
representing the airspace congestion index function, Ec (x, Sc) representing the model of the area to be predicted, the area to be predicted is a circular area, x representing the circle center of the area to be predicted, Sc representing the area to be predicted, Nc representing the number of the heterogeneous aircrafts in the area to be predicted, and D(x,tk) And representing the congestion density of the area to be predicted.
6. The prediction method according to claim 5, wherein before determining whether the probabilistic reachable set region of the heterogeneous aircraft overlaps with the region to be predicted, the method further comprises:
rasterizing the airspace in the height direction to obtain a plurality of height layers, wherein the height layers are corresponding to the airspace [ h ]s,hd]Obtained by dividing according to △ h;
uniformly sampling horizontal areas of all the center heights to obtain a plurality of predicted points;
establishing a region to be predicted by taking each predicted point as a circle center;
the airspace congestion prediction model of the heterogeneous aircraft in the area to be predicted, which takes the circle center as the center, is shown as the following formula IV:
wherein h issRepresents the height, h, corresponding to the lowest layer of the space domaindRepresenting the height corresponding to the highest layer of the airspace, △ h representing the height of each height layer, hjJ1, J, wherein hjJ is a center height of each height layer, J is 1.
7. The prediction method according to claim 1, characterized in that the method further comprises: when the heading angle psi of the heterogeneous aircraftiWhen the angle is equal to 0 °, the first scene formula corresponding to the scene method is specifically shown as the following formula five:
when the heading angle psi of the heterogeneous aircraftiWhen not equal to 0 °, the second scene formula corresponding to the scene method is specifically shown as the following formula six:
wherein S isθ',m(tk)TA transposed area, R, representing the second elliptical probability reachable set areaψRepresenting the twiddle factor.
8. A prediction device for heterogeneous aircraft airspace congestion is characterized by comprising:
the off-line computation model building module of the probability reachable set is used for building the off-line computation model of the probability reachable set of the heterogeneous aircraft according to the planned flight path and the predicted flight path of the heterogeneous aircraft, wherein the predicted flight path is the predicted flight path of the heterogeneous aircraft flying in an airspace;
the judging module is used for judging whether the probability reachable set region of the heterogeneous aircraft is overlapped with the region to be predicted or not according to the offline calculation model of the probability reachable set of the heterogeneous aircraft;
the airspace congestion prediction model establishing module is used for establishing an airspace congestion prediction model of the heterogeneous aircraft according to the probability reachable set area of the heterogeneous aircraft corresponding to the planned flight path when the probability reachable set area of the heterogeneous aircraft is overlapped with the area to be predicted;
the acquisition module is used for acquiring congestion information in the area to be predicted according to the airspace congestion prediction model;
the method for establishing the off-line calculation model of the probability reachable set of the heterogeneous aircraft according to the planned flight path and the predicted flight path of the heterogeneous aircraft comprises the following steps:
establishing a first ellipse probability reachable set model by taking a plurality of sample points in the planned flight path of the heterogeneous aircraft as the circle centers respectively
Optimizing a shape matrix parameter theta in the first elliptical probability reachable set model through an opportunity constraint optimization model, and establishing a second elliptical probability reachable set model meeting a preset probability
According to a plurality of sampling points in the predicted flight path, and a scene method is adopted to carry out approximate solution on the shape matrix parameter theta' in the second ellipse probability reachable set model to obtain the maximumOptimum solution of theta*Establishing an offline calculation model of the probabilistic reachable set of the heterogeneous aircraft
The opportunity constraint optimization model is shown as the following formula I:
wherein, tkRepresents the time of flight of the hetero-aircraft, k ∈ {1sDenotes the planned flight time range of the hetero-aircraft, nsRepresenting a maximum value of the planned time-of-flight range;
Sθ,m(tk) Representing an reachable set of regions with a first elliptical probability of said hetero-aircraft, Sθ',m(tk) A second elliptical probabilistic reachable set region representing the heterogeneous aircraft,
an elliptical probability reachable set region in an offline computational model representing the heterogeneous aircraft probability reachable set, wherein N represents the number of sampling points in the predicted flight path; sθ,m(tk)TA transpose region representing the first elliptical probability reachable set region;
subject to indicates that the … … condition is satisfiedAnd P represents the preset probability,
representing a probability factor.
9. A computer-readable storage medium having computer-executable instructions stored thereon which, when executed by a processor, implement the method of any one of claims 1-7.
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